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Commit 59afe5d8 authored by Shining Sun's avatar Shining Sun
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Merged commits from Toby

parent c923a420
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Showing with 80 additions and 59 deletions
official/resnet/keras/keras_cifar_benchmark.py
View file @ 59afe5d8
......@@ -9,6 +9,7 @@ import tensorflow as tf # pylint: disable=g-bad-import-order
from official.resnet import cifar10_main as cifar_main
import official.resnet.keras.keras_cifar_main as keras_cifar_main
import official.resnet.keras.keras_common as keras_common
DATA_DIR = '/data/cifar10_data/'
......@@ -32,7 +33,7 @@ class KerasCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = self._get_model_dir('keras_resnet56_1_gpu')
flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run_cifar_with_keras(flags.FLAGS)
stats = keras_cifar_main.run(flags.FLAGS)
self._fill_report_object(stats)
def keras_resnet56_4_gpu(self):
......@@ -45,7 +46,7 @@ class KerasCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = ''
flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run_cifar_with_keras(flags.FLAGS)
stats = keras_cifar_main.run(flags.FLAGS)
self._fill_report_object(stats)
def keras_resnet56_no_dist_strat_1_gpu(self):
......@@ -60,7 +61,7 @@ class KerasCifar10BenchmarkTests(object):
'keras_resnet56_no_dist_strat_1_gpu')
flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run_cifar_with_keras(flags.FLAGS)
stats = keras_cifar_main.run(flags.FLAGS)
self._fill_report_object(stats)
def _fill_report_object(self, stats):
......@@ -76,17 +77,14 @@ class KerasCifar10BenchmarkTests(object):
return os.path.join(self.output_dir, folder_name)
def _setup(self):
"""Setups up and resets flags before each test."""
tf.logging.set_verbosity(tf.logging.DEBUG)
if KerasCifar10BenchmarkTests.local_flags is None:
print('Build Flags!!!!')
keras_cifar_main.define_keras_cifar_flags()
keras_common.define_keras_flags()
cifar_main.define_cifar_flags()
# Loads flags to get defaults to then override.
flags.FLAGS(['foo'])
saved_flag_values = flagsaver.save_flag_values()
KerasCifar10BenchmarkTests.local_flags = saved_flag_values
return
print('Restore Flags')
flagsaver.restore_flag_values(KerasCifar10BenchmarkTests.local_flags)
official/resnet/keras/resnet_cifar_model.py
View file @ 59afe5d8
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ResNet56 model for Keras adapted from tf.keras.applications.ResNet50.
"""ResNet50 model for Keras adapted from tf.keras.applications.ResNet50.
# Reference:
- [Deep Residual Learning for Image Recognition](
......@@ -23,7 +23,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import warnings
import tensorflow as tf
......@@ -70,7 +69,12 @@ def _obtain_input_shape(input_shape,
return input_shape
def identity_building_block(input_tensor, kernel_size, filters, stage, block, training):
def identity_building_block(input_tensor,
kernel_size,
filters,
stage,
block,
training=None):
"""The identity block is the block that has no conv layer at shortcut.
Arguments:
......@@ -80,6 +84,8 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
training: Only used if training keras model with Estimator. In other
scenarios it is handled automatically.
Returns:
Output tensor for the block.
......@@ -94,6 +100,7 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
x = tf.keras.layers.Conv2D(filters1, kernel_size,
padding='same',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
......@@ -103,11 +110,12 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=True)
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size,
padding='same',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
......@@ -117,7 +125,7 @@ def identity_building_block(input_tensor, kernel_size, filters, stage, block, tr
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=True)
x, training=training)
x = tf.keras.layers.add([x, input_tensor])
x = tf.keras.layers.Activation('relu')(x)
......@@ -130,7 +138,7 @@ def conv_building_block(input_tensor,
stage,
block,
strides=(2, 2),
training=True):
training=None):
"""A block that has a conv layer at shortcut.
Arguments:
......@@ -141,7 +149,8 @@ def conv_building_block(input_tensor,
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
strides: Strides for the first conv layer in the block.
training: Boolean to indicate if we are in the training loop.
training: Only used if training keras model with Estimator. In other
scenarios it is handled automatically.
Returns:
Output tensor for the block.
......@@ -158,21 +167,23 @@ def conv_building_block(input_tensor,
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = tf.keras.layers.Conv2D(filters1, kernel_size,
x = tf.keras.layers.Conv2D(filters1, kernel_size, strides=strides,
padding='same',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name=conv_name_base + '2a', strides=strides)(input_tensor)
name=conv_name_base + '2a')(input_tensor)
x = tf.keras.layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '2a',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=True)
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
......@@ -182,9 +193,10 @@ def conv_building_block(input_tensor,
name=bn_name_base + '2b',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=True)
x, training=training)
shortcut = tf.keras.layers.Conv2D(filters2, (1, 1), strides=strides,
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
......@@ -193,19 +205,21 @@ def conv_building_block(input_tensor,
shortcut = tf.keras.layers.BatchNormalization(
axis=bn_axis, name=bn_name_base + '1',
momentum=BATCH_NORM_DECAY, epsilon=BATCH_NORM_EPSILON)(
shortcut, training=True)
shortcut, training=training)
x = tf.keras.layers.add([x, shortcut])
x = tf.keras.layers.Activation('relu')(x)
return x
def resnet56(input_shape=None, classes=1000):
def resnet56(input_shape=None, classes=100, training=None):
"""Instantiates the ResNet56 architecture.
Arguments:
input_shape: optional shape tuple
classes: optional number of classes to classify images into
training: Only used if training keras model with Estimator. In other
scenarios it is handled automatically.
Returns:
A Keras model instance.
......@@ -226,74 +240,83 @@ def resnet56(input_shape=None, classes=1000):
x = tf.keras.layers.Conv2D(16, (3, 3),
strides=(1, 1),
padding='valid',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name='conv1')(x)
x = tf.keras.layers.BatchNormalization(axis=bn_axis, name='bn_conv1',
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON)(
x, training=True)
x, training=training)
x = tf.keras.layers.Activation('relu')(x)
# x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_building_block(x, 3, [16, 16], stage=2, block='a', strides=(1, 1),
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='b',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='c',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='d',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='e',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='f',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='g',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='h',
training=True)
training=training)
x = identity_building_block(x, 3, [16, 16], stage=2, block='i',
training=True)
training=training)
x = conv_building_block(x, 3, [32, 32], stage=3, block='a',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='b',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='c',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='d',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='e',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='f',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='g',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='h',
training=True)
training=training)
x = identity_building_block(x, 3, [32, 32], stage=3, block='i',
training=True)
training=training)
x = conv_building_block(x, 3, [64, 64], stage=4, block='a',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='b',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='c',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='d',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='e',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='f',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='g',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='h',
training=True)
training=training)
x = identity_building_block(x, 3, [64, 64], stage=4, block='i',
training=True)
training=training)
x = tf.keras.layers.AveragePooling2D((8, 8), name='avg_pool')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(classes, activation='softmax', name='fc10')(x)
x = tf.keras.layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = tf.keras.layers.Dense(classes, activation='softmax',
kernel_initializer='he_normal',
kernel_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
bias_regularizer=
tf.keras.regularizers.l2(L2_WEIGHT_DECAY),
name='fc10')(x)
inputs = img_input
# Create model.
......
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